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I originally joined Twitter because it was the perfect form of lazy blogging. I could put articles I was reading out into the world with short commentary. No full blog post needed. Now I write more and Twitter feeds what I write about. Despite the fact that Twitter is dumpster fire, I love it, massively.

This book writing thing is messing up my ability to write here regularly. So now I will use my favorite site for lazy blogging as the content for future lazy blog posts here.

My most favorite Twitter finds for the week. I tend to share posts on higher education, international education, and artificial intelligence.

Community colleges are full of innovation and teaching skills for career changers. Traditional universities should look to them for more inspiration:

#highered "Programs like the Lenoir Community College manufacturing academy modify the old approach. They often serve more than one employer, teach skills in demand across an industry, and are open to any students who apply, not just those designated by the client company." https://t.co/0aMhnVkFGO

In AI news, European checkpoints are going to use microexpressions to figure out if you’re lying. If I were writing a full post I’d research whether or not it’s based on the same tech HireVue uses when analyze candidate’s video interviews.

Add this to your #studyabroad orientations. "Answer questions from a computer-animated border guard…. It will then analyse the traveller’s micro-expressions to figure out if he or she is lying." #intledhttps://t.co/ObnVHUCD0E

Also the AI Now Institute (an organization that I am majorly crushing on and want to work for) released their AI Now 2018 Report which presents 10 recommendations for navigating artificial intelligence technology. Everyone should read it. This isn’t the future. This is the reality now.

** Announcement **

Today we launch the AI Now 2018 Report.

It’s been a dramatic year in AI, and more accountability is urgently needed. We share 10 recommendations for industry, governments & academia. Read them here: https://t.co/OjpsincIkD

I wonder what assumptions go into the making of their hiring algorithm… also what happens to candidates who don't fit the pattern? "He conceded that the approach worked less well in new or obscure fields with limited data." https://t.co/04mv0MeFoP

Long haul truck driving sounds like quite the adventure when you’ve got a buddy. Listen to more tales of side hustles and odd jobs (one of my favorite subjects!) from the recent OPB podcast, Tales of a Side Hustle.

Buried at the bottom of an an HBR post titled 8 Ways Machine Learning is Improving Company Processes, is a little nugget about the ways machine learning might soon affect career planning. Machine learning could help employees in navigate their career development by providing:

Recommendations (that) could help employees choose career paths that lead to high performance, satisfaction, and retention. If a person with an engineering degree wishes to run the division someday, what additional education and work experience should they obtain, and in what order?

Could this be a career coach in the future of work? It’s a fascinating idea and I’d love to see it in practice. We’ve already seen machine learning technology take over some parts of a career advisors job. There’s even a chatbot in development that’s trying to be a career coach (let’s hope they’re better than LinkedIn’s mediocre job recommendation algorithm.) IBM uses AI to guide job seekers through their search.

A good career coach will listen to you, help you work out ideas, guide you through an ambiguous process, support you emotionally, and reflect your own words back to you. Machine learning technology can’t do this yet, in answer to my clickbait title.

But there aren’t enough good career coaches to go around. And few people can even afford a good career coach. Moreover, not every organization offers career coaching that helps employees navigate their next steps. Tools that help people navigate a world full of increasingly ambiguous career paths are mighty helpful.

Like many jobs, career coaches won’t be fully replaced by robots or artificial intelligence anytime soon. There will always be people who prefer working with people over machines. But the role of career coaches will change as new tools and technology emerge. Career coaches need to be aware of these changes. The workplace and available roles are shifting rapidly. Career coaches need to be able to coach their clients through these changes. They need to rethink outdated career advice, especially given that our job search is becoming less human. University career departments in particular need to upskill.

Today’s post is brought to you by my half way mark to 50K words for #NaNoWritMo. I’m deep into a chapter on the future of work for my book and still finding a ton of good content to write about. The challenge of course is to write about it and not just read about it. Reading is not writing, I have to remind myself a bajillion times a day.

If you’re into this type of stuff, subscribe and I’ll send you things about careers, future of work, and probably a bunch of gifs.

In the age of big data, a measure-everything mindset is emerging. Julia Ticona, a sociologist and researcher with the Data and Society think tank in New York, says that the same types of apps that track and keep tabs on restaurant workers or delivery people 24/7 are now migrating to white-collar jobs.

But while service and manufacturing industry workers are more used to overt productivity measurements, such systems are often sold to office workers as opportunities to maximize their own productivity, she explains. “For lower wage folks, it’s about scheduling and hours,” says Ticona. “For the white collar folks, it’s about being the ‘best you.’” The inevitable future of Slack is your boss using it to spy on you

There’s so much in this article about all the ways your employer uses new technology and invasive data collection techniques to spy on you at work. There’s even an example of a company that tracks their employees outside of work hours. Your workplace is creeping ever closer to the Circle.

So much of the future of work is focused on robots taking our jobs. But that discussion overlooks much of what’s happening outside of robots, mainly the erosion of employee privacy. The idea that companies should have the rights to all data an employee produces in the course of their workday is absurd. Employee surveillance shouldn’t be normalized. Moreover, we need more discussion about the people making decisions about what constitutes worker productivity. Who are they and how are they qualified to make these decisions? You can bet the executives and upper management aren’t being tracked like this.

I disagree that this is all inevitable. We have the power to say no to it. We have the power to teach emerging leaders how to not to use this technology or point out the potential for abuse. Employee privacy shouldn’t be a trade off for a paycheck. Employees have the power to ask questions: How are you using my personal data? What data are you monitoring? What assumptions are you making about my work when you build productivity measuring algorithms?”

Future employees have the power to ask the right questions during their job interviews. Let’s start teaching people the right questions to ask in an interview for a white collar role. How do you measure success in this role? How do you track worker productivity? How much data do you collect on your employees and what do you use it for?

We’re in the middle of a massive transition to a quantified workplace where leadership wants to measure everything in the pursuit of pure productivity. The people who are impacted most under this system must participate in shaping this transformation and pushing back.

I’m deep into National Writing Month (#NaNoWriMo) and it’s wrecking my ability to write here. I’m in the middle of writing my second book and so far, I’m 14,000 words in for the month of November. For context, I wrote 9,000 words in all of October. The goal of #NaNoWriMo is to write 50,000 words. I’m a little behind but I’m still shooting for it.

On the plus side, #NaNoWriMo month is an excellent tool for aspiring book writers. Things I’ve learned in only two weeks:

The only way you will write a book is to put your ass in a seat and write. Truth.

Writing without self-editing is the hardest part of this month long exercise. I’ll never make it to 50K words if I edit.

Researching writing is not writing your book. Neither is writing about writing a book (which I’m doing now). Writing your book is the only writing that counts towards the goal of publishing a book.

Getting comfortable with the rawness of your words and accepting the messiness is part of the process.

The world is full of people who say they can write better than (insert book here). Like most things, it’s so much harder than it looks.

So in lieu of a post, here’s an article dump on the most interesting things I’ve read this week about AI and ethics, a subject I’m increasingly more interested in. If I weren’t so brain dead from barfing words elsewhere, I’m sure I’d come up with something clever to say about these. But I can’t. So here we are.

Just outstanding. Joy Buolamwini, the founder of notflawless.ai, delivers a spoken word poem on the reality of bias in AI. This organization is a must follow resource for anyone working with AI with topics on the dangers of facial recognition technology and police use of facial recognition tech. Also includes links to books and talks on the subject.

Imagine if every emerging AI engineer read this resources on this site.

“Workers increasingly see assignments and wages doled out by artificial systems rather than human managers, and have to rely on AI, not HR, when things go wrong. According to tech experts, the rise of algorithms is changing not only how we earn a living, but who gets access to jobs and other opportunities — if their data checks out — or not.” – Forbes, Algorithms And ‘Uberland’ Are Driving Us Into Technocratic Serfdom

The Forbes article was referencing the book UBERLAND: How Algorithms Are Rewriting The Rules Of Work, which has just rocketed to the top of my reading list. Until then, I’m definitely looking out for the author on the podcast circuit.

Students are looking for ways to beat AI recruiting tools like HireVue. And now coaching services are offering help:

“A start-up called Finito claims it can coach candidates to beat AI for as long as it takes them to get a job — but at a total cost of nearly £9,000. Candidates are steered through interview dry runs and get tips on what skills are needed to get past robot selections, in sectors including finance, public relations and the arts. They then watch footage back to spot foibles that could be flagged up as nerves.”

Much of the AI and highered interwebs were ablaze in the last week with MIT’s announcement that they’re building an AI college with a cool $1 billion in funding. (side note: I wish I could get into that future interdisciplinary college. Perhaps I’ll just have to wait for the inevitable exec leadership program that comes out of it.)

But I’m far more excited by this news: the African Institute for Mathematical Sciences is launching an African Masters in Machine Intelligence programme. And it’s only 10 months long. Even better? This:

“There are two Rwandans in the first cohort of 35 students, 44 per cent being women – another first at AIMS.”

The students will graduate with a Masters in Mathematical Science specializing in machine learning. The program is backed by tech giants, Google and Facebook. It’s the first of it’s kind in the country.

Maybe Americans might want to considering getting their masters in artificial intelligence abroad. Imagine the perspective they’d gain. Imagine the value they could add to an organization. And they’d do it in less time than a traditional American degree.

A Nigerian colleague shared the article on LinkedIn reminding me yet again of the immense value of global networks.

How much employee data collection is too much? Because it seems our employers – or at least the big corporate ones – want every single piece of your personal data. Is there any option for pushing back on your employer’s personal data grab?

The Kaiser Family Foundation’s annual review of employer-based insurance shows that 21% of large employers collect health information from employees’ mobile apps or wearable devices, as part of their wellness programs — up from 14% last year.